• The current COVID-19 pandemia, caused by the SARS-CoV-2 virus, has caused the death of more than 3.2 million people worldwide.
  

• Studies in convalescent patients has shown a decline of antibody-mediated response a few months after infection.

• CD8+ T cells are critical for efficient clearance of virus-infected cells and long-term protection against SARS-CoV-2.

• DTU HealthTech Department analyzed T cell recognition towards SARS-CoV-2-derived peptides in COVID-19 patients and healthy individuals using DNA-barcoded pMHC multimers combined with a T cell phenotype panel.

• The project utilizes flow cytometry data published by Saini et al., 2021, to make a T cell phenotype analysis by comparing the expression of cell surface markers in COVID-19 patients and healthy individuals to better understand T cell immunity to SARS-CoV-2 infection.

• The COVID-19 pandemic is caused by the highly infectious virus; SARS-CoV-2.

• CD8+ T cells perform essential functions in the control and elimination of viral infections

• Here, we analyze the immunophenotypes of CD8 T cells that can recognize peptides from the SARS-CoV-2 genome

• We compare COVID-19 patients with healthy donors (HD) never exposed to the virus.

• Furthermore, we compare the phenotypes to the severity of the disease.

• We also compare the phenotype of the SARS-CoV-2 specific CD8 T cells with T cells that recognize common viruses.

Phenotype data

• Phenotyping of immune cells provides imformation abouts the activation status of the cells
• This is obtained through antibody staining and flow cytometry, which transforms a fluorescent signal into a voltage.
• Cell populations of specific phenotype is gated in FlowJo software, and frequencies of positive cells can afterwards exported as an .xlsx file

Tidying the data set

• Change NA values
• Add cohort group as a new variable
• Collapse multiple columns into two columns: Gating and Fraction
• Splitting the gating path into two new columns: Parent and Last population

## # A tibble: 1,886 x 8
##    SampleID  cohort_type Hospital_status Gating          T_cell Parent_populati…
##    <chr>     <chr>       <chr>           <chr>           <chr>  <chr>           
##  1 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    CD8+            
##  2 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  3 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  4 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  5 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  6 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  7 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  8 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
##  9 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
## 10 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
## # … with 1,876 more rows, and 2 more variables: Last_population <chr>,
## #   Fraction <dbl>

• .fcs files are the raw data files obtained with flow cytometry.
• In addition to manual gating, these files can be used for clustering and visualizing the cells’ expression levels of multiple parapeters in 2 dimensions with UMAP (Uniform Manifold Approximation and Projection).

• Reading covid_data_augment
• Filtering on Parent and Last population
• Generate plot using ggplot - Boxplot and dotplot
• Multiple plots viewed as facetwrap
• Used geomsigif with Kruskal test

======= ## DISCUSSION